Applications of FPGAs and Reconfigurable Computing: Current Trends and Future Perspectives

Dear Colleagues, 

FPGAs and reconfigurable computing platforms bring significant improvements in implementation efficiency compared to other programmable integrated circuits such as CPUs and GPUs while offering post-fabrication flexibility that is not possible in application-specific integrated circuits (ASICs). Given the fact that conventional von-Neumann architectures suffer from high power densities, power and energy efficiency and cooling become top priorities for scalable high-performance computing. FPGAs allow matching hardware to the application, avoiding overheads, and achieving higher hardware efficiency than other programmable architectures.

Today, FPGAs are heterogeneous systems-on-a-chip (soc) combining reconfigurable fabric with application specific blocks and programmable processors in the form of embedded hard IPs. Given their computational capabilities and energy efficiency, FPGAs are present in most heterogeneous computing systems as accelerators. Applications of FPGAs include accelerators for scientific computing, artificial intelligence/machine learning, embedded systems, real-time systems, digital signal processing, computer vision, computer graphics, hardware emulation, cryptography, and manufacturing systems.

We invite authors from both the academic and industrial communities to contribute original research articles as well as review articles that present applications of FPGAs and other reconfigurable computing architectures in different domains. A simple discussion of applications’ implementations using FPGAs is not considered a sufficient research contribution. Submitted papers should emphasize novel applications/algorithms, novel design techniques/use of architectures, or clearly measured implementation efficiency benefits (especially in comparison to other implementation platforms such as multi-/many core CPUs, GPUs and ASICs).

Relevant topics include but are not limited to:

1. FPGA acceleration of artificial intelligence, machine learning, deep learning and neural networks;
2. Applications of FPGAs in cybersecurity;
3. The role of FPGAs in the cloud and in data centers;
4. FPGAs in high-performance computing;
5. FPGAs in edge/fog/IoT and mobile computing;
6. FPGA acceleration in finance and enterprises (including cryptocurrencies);
7. FPGAs in networks with emphasis on 5G and software-defined networks;
8. FPGAs in space and automotive systems;
9. Trends in education with emphasis on a) the use of FPGAs and FPGA clouds in teaching and research of relevant modules and b) education for FPGAs and reconfigurable computing, including courses, laboratories, teaching experiences;
10. The FPGA market: current status and future perspectives.

Prof. Konstantinos Masselos
Guest Editor

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